Clinical and public health are as much information sciences as they are biomedical sciences, and data are their lifeblood. Nearly all of the myriad activities (or use cases) in clinical and public health (e.g., patient care, surveillance, communit health assessment, policy) involve generating, collecting, storing, analyzing, or sharing data about individual patients or populations. Effective clinical and public health practice in the twenty-first century requires access to data from an increasing array of information systems, including but not limited to electronic health records. However, the quality of data in electronic health record systems has been shown to be poor or unfit for use across a number of use cases like surveillance and policy. In addition, methods for measuring the quality of data in information systems are nascent. This presents an opportunity for the development of improved methods for assessing and improving the quality of data in electronic systems. In the proposed project, we will use a Health Data Stewardship framework to guide the development and testing of methods that measure data quality in large observational health data sets. Specifically, we will 1) extend the Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES) software to measure the quality of data electronically reported from disparate information systems to public health agencies for disease surveillance; and 2) apply the ACHILLES extensions to explore the quality of data captured from multiple real-world health systems, hospitals, laboratories, and clinics. We will further demonstrate the extended software to public health professionals, gathering feedback on the ability of the methods and software tool to support public health agencies' efforts to routinely monitor the quality of data received for surveillance of disease prevalence and burden. Furthermore, because the ACHILLES software is available as open-source and supported by multiple health systems, our work may be applicable to other organizations that seek to characterize the quality of large scale observational data sets for other use cases including comparative effectiveness research (CER), patient centered outcomes research (PCOR), and pharmacoepidemiology.